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// STL
#include <iostream>
#include <fstream>
#include <algorithm>
#include <iterator>
// Boost
#include <boost/program_options/parsers.hpp>
#include <boost/program_options/variables_map.hpp>
#include <boost/scoped_ptr.hpp>
// Local
#include "pyp-topics.hh"
#include "corpus.hh"
#include "contexts_corpus.hh"
#include "gzstream.hh"
static const char *REVISION = "$Rev$";
// Namespaces
using namespace boost;
using namespace boost::program_options;
using namespace std;
int main(int argc, char **argv)
{
cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
cout << REVISION << '\n' <<endl;
////////////////////////////////////////////////////////////////////////////////////////////
// Command line processing
variables_map vm;
// Command line processing
{
options_description cmdline_specific("Command line specific options");
cmdline_specific.add_options()
("help,h", "print help message")
("config,c", value<string>(), "config file specifying additional command line options")
;
options_description config_options("Allowed options");
config_options.add_options()
("data,d", value<string>(), "file containing the documents and context terms")
("topics,t", value<int>()->default_value(50), "number of topics")
("document-topics-out,o", value<string>(), "file to write the document topics to")
("default-topics-out", value<string>(), "file to write default term topic assignments.")
("topic-words-out,w", value<string>(), "file to write the topic word distribution to")
("samples,s", value<int>()->default_value(10), "number of sampling passes through the data")
("backoff-type", value<string>(), "backoff type: none|simple")
// ("filter-singleton-contexts", "filter singleton contexts")
("hierarchical-topics", "Use a backoff hierarchical PYP as the P0 for the document topics distribution.")
("freq-cutoff-start", value<int>()->default_value(0), "initial frequency cutoff.")
("freq-cutoff-end", value<int>()->default_value(0), "final frequency cutoff.")
("freq-cutoff-interval", value<int>()->default_value(0), "number of iterations between frequency decrement.")
("max-threads", value<int>()->default_value(1), "maximum number of simultaneous threads allowed")
("max-contexts-per-document", value<int>()->default_value(0), "Only sample the n most frequent contexts for a document.")
("num-jobs", value<int>()->default_value(1), "allows finer control over parallelization")
("temp-start", value<double>()->default_value(1.0), "starting annealing temperature.")
("temp-end", value<double>()->default_value(1.0), "end annealing temperature.")
;
cmdline_specific.add(config_options);
store(parse_command_line(argc, argv, cmdline_specific), vm);
notify(vm);
if (vm.count("config") > 0) {
ifstream config(vm["config"].as<string>().c_str());
store(parse_config_file(config, config_options), vm);
}
if (vm.count("help")) {
cout << cmdline_specific << "\n";
return 1;
}
}
////////////////////////////////////////////////////////////////////////////////////////////
if (!vm.count("data")) {
cerr << "Please specify a file containing the data." << endl;
return 1;
}
assert(vm["max-threads"].as<int>() > 0);
assert(vm["num-jobs"].as<int>() > -1);
// seed the random number generator: 0 = automatic, specify value otherwise
unsigned long seed = 0;
PYPTopics model(vm["topics"].as<int>(), vm.count("hierarchical-topics"), seed, vm["max-threads"].as<int>(), vm["num-jobs"].as<int>());
// read the data
BackoffGenerator* backoff_gen=0;
if (vm.count("backoff-type")) {
if (vm["backoff-type"].as<std::string>() == "none") {
backoff_gen = 0;
}
else if (vm["backoff-type"].as<std::string>() == "simple") {
backoff_gen = new SimpleBackoffGenerator();
}
else {
cerr << "Backoff type (--backoff-type) must be one of none|simple." <<endl;
return(1);
}
}
ContextsCorpus contexts_corpus;
contexts_corpus.read_contexts(vm["data"].as<string>(), backoff_gen, /*vm.count("filter-singleton-contexts")*/ false);
model.set_backoff(contexts_corpus.backoff_index());
if (backoff_gen)
delete backoff_gen;
// train the sampler
model.sample_corpus(contexts_corpus, vm["samples"].as<int>(),
vm["freq-cutoff-start"].as<int>(),
vm["freq-cutoff-end"].as<int>(),
vm["freq-cutoff-interval"].as<int>(),
vm["max-contexts-per-document"].as<int>(),
vm["temp-start"].as<double>(), vm["temp-end"].as<double>());
if (vm.count("document-topics-out")) {
ogzstream documents_out(vm["document-topics-out"].as<string>().c_str());
int document_id=0;
map<int,int> all_terms;
for (Corpus::const_iterator corpusIt=contexts_corpus.begin();
corpusIt != contexts_corpus.end(); ++corpusIt, ++document_id) {
vector<int> unique_terms;
for (Document::const_iterator docIt=corpusIt->begin();
docIt != corpusIt->end(); ++docIt) {
if (unique_terms.empty() || *docIt != unique_terms.back())
unique_terms.push_back(*docIt);
// increment this terms frequency
pair<map<int,int>::iterator,bool> insert_result = all_terms.insert(make_pair(*docIt,1));
if (!insert_result.second)
all_terms[*docIt] = all_terms[*docIt] + 1;
//insert_result.first++;
}
documents_out << contexts_corpus.key(document_id) << '\t';
documents_out << model.max(document_id).first << " " << corpusIt->size() << " ||| ";
for (std::vector<int>::const_iterator termIt=unique_terms.begin();
termIt != unique_terms.end(); ++termIt) {
if (termIt != unique_terms.begin())
documents_out << " ||| ";
vector<std::string> strings = contexts_corpus.context2string(*termIt);
copy(strings.begin(), strings.end(),ostream_iterator<std::string>(documents_out, " "));
std::pair<int,PYPTopics::F> maxinfo = model.max(document_id, *termIt);
documents_out << "||| C=" << maxinfo.first << " P=" << maxinfo.second;
}
documents_out <<endl;
}
documents_out.close();
if (vm.count("default-topics-out")) {
ofstream default_topics(vm["default-topics-out"].as<string>().c_str());
default_topics << model.max_topic() <<endl;
for (std::map<int,int>::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) {
vector<std::string> strings = contexts_corpus.context2string(termIt->first);
default_topics << model.max(-1, termIt->first).first << " ||| " << termIt->second << " ||| ";
copy(strings.begin(), strings.end(),ostream_iterator<std::string>(default_topics, " "));
default_topics <<endl;
}
}
}
if (vm.count("topic-words-out")) {
ogzstream topics_out(vm["topic-words-out"].as<string>().c_str());
model.print_topic_terms(topics_out);
topics_out.close();
}
cout <<endl;
return 0;
}
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